Explaining Best Decisions via Argumentation.
Qiaoting Zhong, Xiuyi Fan, Francesca Toni, Xudong Luo · 2014
Abstract. This paper presents an argumentation-based multi-attribute decision making model, where decisions made can be explained in natural language. More specifically, an explanation for a decision is obtained from a mapping between the given decision framework and an argumentation framework, such that best deci-sions correspond to admissible sets of arguments, and the explanation is gener-ated automatically from dispute trees sanctioning the admissibility of arguments. We deploy a notion of rationality where best decisions meet most goals and ex-hibit fewest redundant attributes. We illustrate our method by a legal example, where decisions amount to past cases most similar to a given new, open case. 1